A Monte Carlo Evaluation of the Stock Synthesis Assessment Program

A Monte Carlo Evaluation of the Stock Synthesis Assessment Program

D.B. Sampson and Y. Yin

A Monte Carlo Evaluation of the Stock Synthesis Assessment ProgramThis is part of Fishery Stock Assessment Models
Format Price  
PDF download [573.1 KB]
Bypass cart and download
Free Add to Cart


Stock assessments for many U.S. Pacific coast groundfish stocks are developed using the catch at age method known as Stock Synthesis. The Stock Synthesis computer program attempts to reconstruct the demographic history of a stock from observed changes in fish age or size distributions, coupled with auxiliary information such as an index of stock biomass developed from a research survey or an index of fishing mortality based on fishing effort. In this study Monte Carlo simulation techniques were used to generate fishery and survey data with known characteristics. The simulated data were then analyzed with the age-structured version of the Stock Synthesis program and results from the program were compared with the true values to evaluate the influence of measurement errors on the accuracy of the Stock Synthesis results. Data sets were constructed with low and high levels of random error in each of four types of sample data (fishery age composition, a fishing effort index, survey age composition, and a survey index of stock biomass). A series of experiments, based on a fractional factorial design, was conducted to examine the importance of eight factors: low versus high rates of natural mortality; constant versus variable annual recruitment; low versus high rates of increase in fishing mortality; dome-shaped versus asymptotic fishery selectivity; short versus long data series; low versus high variability in the fishing effort index; low versus high variability in the survey biomass index; and small versus large samples for age composition. On average the Stock Synthesis estimates for total biomass, exploitable biomass, recruitment, and fishing mortality in the ending year were slightly positively biased (3.5-6.1%) but less variable than the input data. In general, the number of years in the data series and the size of the age samples were the most influential factors, with increased amounts of data producing less biased and less variable estimates.

Item details